Estimating Prevalence of Post-war Health Disorders Using Capture-recapture Data

Effective surveillance on the long-term public health impact due to war and terrorist attacks remain limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size of the population under the risk of physical and mental health hazards is of utmost necessity. In this context, multiple system estimation is a potential strategy that has recently been applied to quantify under-reported events allowing heterogeneity among the individuals and dependence between the sources of information. To model such complex phenomena, a novel trivariate Bernoulli model is developed, and an estimation methodology using Monte Carlo based EM algorithm is proposed. Simulation results show superiority of the performance of the proposed method over existing competitors and robustness under model mis-specifications. The method is applied to analyze real case studies on the Gulf War and 9/11 Terrorist Attack at World Trade Center, US. The results provide interesting insights that can assist in effective decision making and policy formulation for monitoring the health status of post-war survivors.Comment: arXiv admin note: text overlap with arXiv:2105.0867

Estimation of Population Size with Heterogeneous Catchability and Behavioural Dependence: Applications to Air and Water Borne Disease Surveillance

Population size estimation based on the capture-recapture experiment is an interesting problem in various fields including epidemiology, criminology, demography, etc. In many real-life scenarios, there exists inherent heterogeneity among the individuals and dependency between capture and recapture attempts. A novel trivariate Bernoulli model is considered to incorporate these features, and the Bayesian estimation of the model parameters is suggested using data augmentation. Simulation results show robustness under model misspecification and the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse real case studies on epidemiological surveillance. The results provide interesting insight on the heterogeneity and dependence involved in the capture-recapture mechanism. The methodology proposed can assist in effective decision-making and policy formulation

Causal Analysis at Extreme Quantiles with Application to London Traffic Flow Data

Transport engineers employ various interventions to enhance traffic-network performance. Recent emphasises on cycling as a sustainable travel mode aims to reduce traffic congestion. Quantifying the impacts of Cycle Superhighways is complicated due to the non-random assignment of such an intervention over the transport network and heavy-tailed distribution of traffic flow. Treatment effects on asymmetric and the heavy-tailed distributions are better reflected at extreme tails rather than at averages or intermediate quantiles. In such situations, standard methods for estimating quantile treatment effects at the extremes can provide misleading inference due to the high variability of estimates. In this work, we propose a novel method to estimate the treatment effect at extreme tails incorporating heavy-tailed feature in the outcome distribution. Simulation results show the superiority of the proposed method over existing estimators for quantile causal effects at extremes. The analysis of London transport data utilising the proposed method indicates that the traffic flow increased substantially after the Cycle Superhighway came into operation. The findings can assist government agencies in effective decision making to avoid high consequence events and improve network performance.Comment: arXiv admin note: text overlap with arXiv:2003.0899